High-School Dropouts and Transitory Labor Market … fenoll...High-School Dropouts and Transitory...

High-School Dropouts and Transitory Labor Market

Shocks: The Case of the Spanish Housing Boom

Ainhoa Aparicio∗†

Collegio Carlo Alberto

April 22, 2011

Abstract

This paper addresses the implications of transitory changes in labor market con-

ditions for low versus high educated workers on the decision to acquire education. To

identify this effect, I use the improvement in labor market prospects of low educated

workers motivated by the increases in construction employment and wages during

the recent housing boom. The estimation strategy is based on the fact that changes

in the labor market driven by the construction sector affect only men. Increases

in construction activity are found to increase men’s relative to women’s propensity

to drop out of high-school. According to this finding, policies promoting education

should strengthen when in the presence of transitory shocks in the labor market

that make dropping out more attractive.

JEL codes: J24, J22, I20, L74

Keywords: High-school dropout, housing boom, Spain

∗Collegio Carlo Alberto. Address: Via Real Collegio 30, Moncalieri (TO) 10024, Italy.

Email: [email protected]†I would like to thank Libertad Gonzalez for her invaluable advice and support. This paper has bene-

fited from comments by Ghazala Azmat, Jose Ignacio Garcıa Perez, Kurt Schmidheiny, Nuria Rodrguez

Planas, Thijs Van Rens, Julian Messina and Saku Aura.

1

1 Introduction

The 2006 European Commission Progress Report on Education Policies states that ”the

high number of [high-school dropouts] is an obstacle to securing access to the knowledge-

based society and greater social cohesion in the European Union”1. The European Union

Government established an objective for 2010 of a 10% rate of high-school dropouts, as

defined by persons aged 18 to 24 who have attained at most lower secondary education

and who declare not having received any education or training in the previous four weeks.

This objective was still unfulfilled in 2009, when the proportion of early school leavers

in the EU-27 was more than 15%. In this paper, I show that changes in labor market

conditions in recent years have prevented the achievement of a lower high-school dropout

rate, specially for males.

Agents tend to abandon productivity enhancing activities and concentrate on produc-

ing when production becomes more profitable 2. Likewise, in the context of education,

individuals would decide to acquire less education when participating in the labor market

is more rewarding. The recent housing boom has altered the labor market opportunities

faced by youngsters and is therefore likely to be responsible for changes in high-school

dropout trends.

Having a high-school diploma has been shown to reduce unemployment and increase

wages. These differences are unrelated to individual characteristics as argued by Stern,

Paik, Catterali, and Nakata (1989). I show that, by reducing the negative consequences

of not obtaining a high-school diploma, the recent housing boom has induced more male

youngsters to drop out of the educational system before getting a high-school diploma.

From a policy perspective, this finding implies that creating the right incentives to get

educated even in the presence of transitory labor market shocks can help to avoid high

unemployment rates at the end of the economic upturn.

1Along this paper I will use the notion of early school leavers as defined by the European Union, butI will refer to early school leavers under the more common denomination of high-school dropouts.

2Some papers that find that productivity enhancing activities are reduced during economic expansionsare: Bean (1990), Gali and Hammour (1992), Saint-Paul (1993), and Malley and Muscatelli (1999).

2

The construction sector presents two features that make it appropriate to estimate

the effect of improvements in employment prospects for low educated individuals on the

decision to drop out of school. First, it employs mainly men and second, the proportion

of low educated workers is higher than in the rest of the economy. This implies that

the upturn in construction activity will predominantly affect low educated men, and this

will allow us to estimate the impact of better employment opportunities for low educated

workers by comparing male to female high-school dropout probabilities. Additionally, the

magnitude of the increase in construction activity and the differences in the incidence of

the housing boom across regions provides the necessary variation to correctly identify the

effect.

The implications of the housing boom for high-school dropouts are tested using Spanish

data. The case of Spain is of special interest because of the situation of its educational

system as well as the magnitude of the recent housing boom. Spain is one of the countries

with the highest incidence of high-school dropouts in the OECD. This incidence has risen

in the last decade, reaching 31% in 20093. Additionally, while in the US house prices

increased by 104% between 1997 and 2007, prices in Spain boosted by more than 190% in

that same period (see Figure 1). Regarding the intensity of construction activity in the

period, the ratio of construction value added over GDP and the number of new dwellings

experienced a boost of 66% and 90%, respectively (see Figures 2 and 3).

In 2005, more than 29% of high-school students abandoned their studies to enter the

workforce in Spain. Out of all individuals that left high-school to work, more than 23%

of them worked in the construction sector. Disaggregating by gender, 32.61% of all males

that leave high-school to work, do it in the construction sector, while the corresponding

figure for females is negligible4.

Some earlier papers have devoted their attention to the role of labor market conditions

3Data source: Eurostat. Information is publicly available athttp://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&language=en&pcode=tsisc060.The primary source is the EU Labor Force Survey.

4These data are obtained from the 2005 Spanish Education and Labor Transitions Survey (Encuestade Transicion Educativo-Formativa e Insercion Laboral (2005)).

3

in explaining student drop out decisions. The vast majority of previous studies have

addressed this question by including macroeconomic indicators in the equation modeling

the probability of dropping out.

Youth unemployment rate has been found to have a negative impact on dropouts in the

studies by Peraita and Pastor (2000) and Clark (2011). The same conclusion is reached

when labor market conditions are measured by the general unemployment rate in Duncan

(1965) and Rees and Mocan (1997). However, the general unemployment rate displays

a positive impact when both, the youth and general unemployment rate, are considered

simultaneously by Petrongolo and San Segundo (2002). Regarding the effect of wages,

Neumark and Wascher (1995) and Black, McKinnish, and Sanders (2005) find that wages

of unskilled workers affect dropouts positively. In contrast, Schady (2004) conclude that

there is no effect of labor market conditions on dropouts.

The closest paper to this one is Black, McKinnish, and Sanders (2005). They find

that an increase in the earnings of low-skilled workers decreases high-school enrolment.

They make use of the exogenous variation in low-skilled earnings caused by the coal

boom and bust. However, gender-specific data is not available for their study. In my

paper, I take advantage of gender-specific data to identify the effect of changes in labor

market prospects, considered as the interaction of employment opportunities and wages,

on education decisions.

The recent Spanish housing boom was caused by the confluence of financial as well as

demand factors (see Gonzalez and Ortega (2009)) and had a great influence on local labor

markets. It improved the labor market prospects of high-school dropouts with respect to

those of more educated workers. Theoretically, the gains from leaving the school system

are represented by the discounted sum of the interactions of probability of employment

and expected wage in each period if one is low educated, minus the discounted sum of

the interactions of probability of employment and expected wage in each period if one

is highly educated5. This argument originated in the classical theory of Becker (1994).

5This argument is made assuming a binary decision of whether to complete high-school. This is a

4

In the last decade, both employment opportunities and wages have increased sharply in

the construction sector. This raises the gains from leaving the school system for men.

Figure 4 shows the huge increase in employment in the construction sector. Figure 5

makes apparent that construction wages have risen more than the average wage in the

economy. In fact, the average construction wage was lower than the average wage at the

beginning of the housing boom and it became higher afterwards. Given that construction

employs low educated workers intensively as compared with the overall economy, agents

in labor markets where the housing boom was more pronounced are expected to leave

school earlier6.

The previous hypothesis is tested using individual data from the Spanish Labor Force

Survey in combination with data from the Spanish Regional Accounts and the Spanish

Ministry of Housing. The sample is composed by individuals aged 18 to 24, out of which

one third are high-school dropouts. Men represent 61% of total high-school dropouts.

There is notable variation across regions within Spain. The highest ratios are found in

some Mediterranean regions like Alicante and Almerıa, where they reach more than 42%,

and the lowest ratios correspond to the Northern regions, i.e., Navarra and Paıs Vasco,

where high-school dropouts are less than 20%. Regarding the evolution over time, the

high-school dropout rate continuously decreased since 1995 when the high-school dropout

rate was around 40%, until 2000 when it reached a minimum of 28%. Since then, it has

remained more or less stable until 2008, when it exceeded 30%.

The empirical correlation between the proportion of high-school dropouts and the

intensity of the housing boom as measured by the ratio between construction and total

value added in each province is positive for both males and females. This is represented

in Figure 6. The line that fits the relation between high-school dropouts and construction

activity is however steeper for males (the coefficient is 0.55 versus 0.25 of females) and,

plausible assumption in our case because Spanish high-school students rarely work.6According to the data obtained from the Spanish Labor Force Survey, the average share of high-school

dropouts working in construction for the period 1995-2007 is 73.4%. In contrast, the average share ofhigh-school dropouts working in the overall economy for that same period is only 52.6%.

5

moreover, the slope is significant for males, not significant for females and statistically

different from males and females. This illustrates the findings in the main econometric

specification, where a differential positive effect of construction activity on high-school

dropouts is found for males relative to females, while the average effect on all individuals

is positive but insignificant.

The causal relationship between high-school dropouts and construction activity in a

regional labor market is estimated by using the fact that changes in construction activity

change employment prospects of men while it leaves those of women practically unaffected.

The evolution of male and female employment in construction is displayed in Figure 7.

Women have always represented a negligible fraction of construction workers.

Empirical results show that variations in construction activity as measured by changes

in construction over total value added in a region make men more likely to drop out of high-

school, while women remain unaffected. This effect is consistent with the estimations using

number of new dwellings as an alternative measure of construction activity. In addition to

the contemporaneous effect, I also find a significant positive effect of construction activity

at the age of 17, i.e., at the time when the decision to drop out of high-school is more

likely to be taken.

I conclude that changes in labor market prospects for low relative to high educated

workers, even if they are transitory in nature, can have significant effects on schooling

decisions.

The remainder of the paper proceeds as follows. Section 2 presents the methodol-

ogy used to estimate the effect of the changes in labor market conditions as a result of

the housing boom on high-school dropout rates. Section 3 describes the databases, the

variables and the sample used in the analysis. Section 4 discusses the empirical results

and explores the effect of changes in the level of activity in other economic sectors on

high-school dropouts. Finally, section 5 concludes.

6

2 Methodology

The aim of the empirical exercise is to test whether changes in labor market conditions for

low versus high educated workers change individual decisions to drop out of high-school.

The construction sector is characterized by employing mostly men. In fact, the aver-

age proportion of Spanish women employed in construction from 1980 to 2008 was only

5%. I take advantage of this feature of the construction sector and identify the effect of

variations in labor market prospects on high-school dropout by exploring the differential

effect of variations in construction activity on male with respect to female dropout prob-

ability. Thus, the explanatory variable of interest is defined as the interaction between

construction activity and a male dummy.

The probability of high-school dropout is assumed to have a logistic form and is then

estimated by means of the following equation:

yijt =exp(βZijt)

1 + exp(βZijt)

with

βZijt = β0 + β1Cjt ·maleijt + β2Cjt + β3maleijt + β4Xijt + β5Wjt + β6ηj + β7ξt

where y is an indicator variable equal to one if individual i living in province j at year

t is a high-school dropout and zero otherwise, C measures the intensity of construction

activity, male is a dummy taking value 1 if the individual is a man and 0 if a woman, X

is a vector of individual and family characteristics including age, an indicator for being an

immigrant, a dummy for high-school graduated father, a binary variable for high-school

graduated mother, an indicator for university graduated father, a dummy for university

graduated mother, a binary variable for father working, an indicator for mother working,

a dummy for absent mother, a binary variable for absent father, an indicator for living

without any of the parents, number of brothers, number of sisters, number of siblings aged

less than 10, number of siblings aged 11 to 15, number of siblings aged 16 to 20, number

of siblings aged 21 to 30 and number of high-school dropout siblings, W represents three

7

variables varying at the regional level, namely, total value added, youth unemployment

rate, and overall unemployment rate, η is a vector of province dummies, and ξ includes

year dummies7.

The high-school dropout rate in a province is likely to exhibit time dependence. To

account for Bertrand, Duflo, and Mullainathan (2004)’s critique, standard errors are clus-

tered at the province level8.

Taking equation 1 as a base, I estimate the contemporaneous effect of construction

activity on high-school dropouts. The estimated coefficient β1 is reflecting a combination

between the influence of construction activity on the probability of leaving the education

system and the probability of staying out of the system. This happens because many

individuals may have taken their decision to drop out before they were interviewed. In

particular, some youngsters may have actually taken their decisions to drop out of high-

school at the average age at which students are in high-school. As an attempt to isolate

the effect on leaving high-school, I estimate the differential effect of construction activity

when the individual was 17 years old for males with respect to females 9. Therefore, the

key explanatory variable in this specification is the interaction of construction activity at

the age of 17 and a male dummy. The estimated equation remains as follows:

yijt =exp(βZijt)

1 + exp(βZijt)

with

βZijt = β0+β1C17jt ·maleijt+β2C

17jt +β3Cjt+β4maleijt+β5Xijt+β6W

17jt +β7ηj +β8ξt+εijt

7The information on parents’ education and employment status is only available for cohabiting fathersand mothers. Hence, the dummies for absent father and absent mother serve to account for the missingobservations.

8The paper by Bertrand, Duflo, and Mullainathan (2004) calls our attention to the fact that esti-mated standard errors are inconsistent when outcomes are serially correlated and the estimation is doneemploying difference-in-differences techniques.

9The study of both, contemporaneous and at the age of 17, effects stablished a paralelism with the USpolicy indicators of h-s graduation rate. In particular, the US National Center for Educational Statisticspublishes two measures of the high-school graduation rate, the high-school status completion rate (thepercentage of 18- to 24- years old possessing a high-school credential) and the 17-years old graduationratio (the number of high-school diplomas issued by secondary school each year divided by the size of the17-years old population in that year).

8

where C17 reflects the construction activity measure when the individual i was 17 years

old, W 17contains total value added in the province at the age of 17, youth unemployment

rate, and overall unemployment rate. The rest of variables are as defined above10.

As in the equation for the contemporaneous effect, standard errors are clustered at

the province level.

3 Data and descriptive statistics

3.1 Data sources and sample definition

The data used in the estimations contains individual data from the Spanish Labor Force

Survey as well as province level data from the Spanish Regional Accounts and the Spanish

Ministry of Housing.

The Spanish Labor Force Survey has been collected quarterly since 1976. In practice,

more than 60000 households containing around 180000 individuals are surveyed in each

wave. The information contained in the survey includes individual characteristics such

as age, gender and level of education as well as details about working status and family

characteristics. The population of interest is formed by working age individuals, i.e.,

individuals aged 16 to 65 years old.

The sample selected for the analysis is composed by individuals aged 18 to 24 years

old. These individuals constitute the set of relevant high-school dropouts as defined by

the EU and US governments. Only the waves corresponding to the fourth quarter of

each year are included. I have chosen the fourth quarter because the decision to drop

out of high-school will be observed at the beginning of the academic year which starts

in September and then new dropouts will only be observable in the fourth quarter of the

10I have excluded the contemporaneous total value added in the equation because it has proven to behighly collinear with total value added at 17. This should not be a concern because the high correlationexisting between total value added and total value added at 17 assures that I am controlling adequatelyfor the impact of economic activity on high-school dropout. Moreover, the rest of estimated coefficientsare statistically invariant to the inclusion of contemporaneous total value added.

9

year. The time period covered comprises the years from 1997 to 2007. This is the period

when the construction boom took place.

The Spanish Regional Accounting data follows the methodology established by the

1995 European System of National and Regional Accounts. The data contains informa-

tion on GDP decomposed by economic sectors defined as agriculture, energy, industry,

construction and services. The information is geographically disaggregated up to the

province level. The data has yearly frequency and is available for the period including

the years 1995 to 2007.

The Spanish Ministry of Housing collects administrative data on dwelling acquisition,

sole prices and dwelling construction. In the analysis, the number of new dwellings will be

used11. This data is obtained from a registry containing architect’s construction permis-

sions. Hence, the reported information is very accurate and comprises the whole universe

of dwellings in Spain. The data is aggregated at the province and year level and covers

the period 1991 to 2008.

3.2 Variable definition and descriptive statistics

The variable high-school dropout is defined according to the European Union criteria and,

consequently, it is a binary indicator equal to one if an individual fulfills the following two

conditions: (i) her level of education is inferior to high-school graduate and (ii) declared

not to have followed any official education in the preceding four weeks, and zero otherwise.

The explanatory variable of interest is intensity of construction activity. This is mea-

sured as construction over total value added in the province of residence during the year

when the individual is surveyed. This measure is interacted with a male dummy in order

to address the differential effect of construction activity on males compared to females.

When exploring the effect of construction activity at the age of 17 on high-school

11The number of new dwellings is a good proxy of expectations about employment in the constructionsector. In fact, Figure 3 shows that the number of new dwellings is the measure that best captures theend of the housing boom.

10

dropouts, the value assigned to each individual corresponds to the measure of construction

activity at the time she was 17 years old. This restricts the time period included in the

sample because the information on construction activity at 17 is not available for all

individuals in the years 1997 to 2001. To avoid sample selectivity issues, only the years

for which this information is available for all individuals (2002 to 2007) are included in

the analysis.

The descriptive statistics for the variables included in the main regression are displayed

in Table 1. The final sample is composed by 186466 individuals. One third of them are

high-school dropouts and around one half of the sampled individuals are male.

Average construction over total value added assigned to individuals in the sample is

around 10% and the average number of constructed dwellings per individual aged 18 to

24 is 0.037.

The reported figures are similar to the ones for the sample included in the regression

estimating the effect of construction activity at age 17. These are displayed in Table 2.

The figures are fairly similar to the ones presented in Table 1. Due to the shorter time

spam covered, the number of individuals is reduced to 86953.

Table 3 contains the descriptive statistics for the sample included in the regressions

where construction activity at 17 is measured using number of new dwellings at 17. Again,

there are not significant changes with respect to the samples described in the two previous

tables. In this case, the number of observations is 143440.

4 Empirical results

The objective of the empirical exercise is to test whether changes in labor market condi-

tions that improve labor market perspectives for low educated workers make more likely

that individuals dropout of high-school. The identification strategy makes use of the fact

that men experience better employment opportunities as a result of an increase in con-

struction activity while women do not. In practice, I focus on the sign of the coefficient

11

associated to the interaction of construction activity and a male dummy in the equation

for the probability of being a high-school dropout. A positive and significant value of this

coefficient is interpreted as transitory improvements in labor market prospects for low

educated workers inducing a rise in the probability of high-school dropout.

4.1 Contemporaneous effect

In this section, I discuss the estimated differential effect of changes in regional construction

activity over time on high-school dropout probability for males versus females. Table 4

displays the results for the estimation in which the probability of dropping out of high-

school is assumed to have a logistic functional form. The impact of construction activity

on male versus female high-school dropout probability is positive and significant. This

corroborates the hypothesis that labor market shocks that improve employment prospects

for low educated workers significantly increase the likelihood that individuals dropout

of high-school. The estimated coefficient associated to the variable construction activity

interacted with male in the logistic equation is 2.657. This is interpreted as a one standard

deviation increase in construction activity as measured by the share of total value added

that is produced by the construction sector inducing an increment in the probability

of high-school dropout for the average male individual of 1.31 percentage points. The

estimated coefficient is arguably robust to the inclusion of the different set of controls.

One could argue that the intensity of construction activity may affect the high-school

dropout rate not through labor market conditions but through affordability of housing.

The latter channel could operate such that when construction activity increases, housing

prices decrease and more individuals dropout of high-school to start working and buy

a house. However, in our data, the level of construction activity is positively correlated

with housing prices. Hence, if the previous critique was applicable, more intense construc-

tion activity together with higher prices would induce less dropouts. Additionally, the

estimation results show that men dropout more in the presence of higher housing prices.

This contradicts the critique because it is male working status what usually determines

12

household formation. Hence, our identification strategy is assuring that the estimated

effect is not a consequence of changes in affordability of housing.

Another source of concern could be that the recent housing boom is contemporaneous

to an important economic upturn and, therefore, the estimated effect could be a conse-

quence of men being differently affected by the economic cycle. In unreported regressions,

I have included the interaction of total value added and a male dummy as an additional

control variable. The associated coefficient is negative and insignificant. This constitutes

additional evidence that the effect of the economic cycle, if there is any, is stronger on

women. Additionally, previous evidence shows that women’s labor supply is more affected

by the economic cycle. See Sabarwal, Sinha, and Buvinic (2011) for a recent review of

the evidence. This indicates that, if the economic cycle was influencing our results, the

actual effect would be higher in magnitude than the estimated one.

Additionally, I discuss the potential existence of reverse causality, i.e., variations in

high-school dropouts causing changes in construction activity. Our results could be ex-

plained by reverse causality if increments in the high-school dropout rate in the province

reduce construction wages and therefore incentive construction activity by increasing its

profitability. However, as it was previously noted, construction wages rose more than

wages in other sectors during the period of study. Moreover, labor costs grew more than

the rest of production costs in the construction sector. Therefore, if the labor supply of

high-school dropouts has some influence on construction activity through wages, our coef-

ficient of interest would be downward biased. Additionally, the literature on the causes of

the housing boom (see Glaeser, Gyourko, and Saiz (2008) for a recent example) indicates

that labor supply does not play a role in explaining the expansion of the construction sec-

tor in recent years. Therefore, I expect no impact of reverse causality in the estimations.

In the empirical analysis I define the relevant labor market for an individual at the

province level. Given the low geographical mobility of the Spanish population, this is

considered a reasonable assumption12. To assure that migration across provinces is not

12The low mobility of the individuals in the sample is illustrated by the fact that more than 87.6% of

13

determining the results, I performed two additional specifications. First, construction

activity in the province of residence is substituted by construction activity in the province

of birth. Second, construction activity in the province of birth is used as an instrument

for construction activity in the province of residence. Both estimations lead to results

that are consistent with the ones presented in Table 4.

Appendix A presents the rest of coefficients in the logistic regression. One observes that

males are more likely to drop out of high-school. The influence of age could be represented

using an inverted U-shape with a maximum in 21 years old. Immigrants are significantly

more likely to drop out. Individuals are less likely to drop out if their parents are more

educated and if their parents are working. This is coherent with the findings of Maani

and Kalb (2007) that economic resources play a significant role in school leaving decisions.

Finally, those living without their fathers are less likely to drop out of high-school while

living without their mothers or on one’s own are associated to a higher probability of

being a high-school dropout. Having more brothers and sisters is negatively correlated

with the probability of dropout. The effect is less pronounced for younger siblings. The

probability of dropout is positively correlated among siblings. Higher total value added in

the province is positively associated with dropouts. The positive coefficient associated to

the youth unemployment rate and the negative coefficient for the general unemployment

rate are coherent with the findings of Petrongolo and San Segundo (2002). However,

the coefficient for the youth unemployment rate is not statistically significant due to the

correlation of youth unemployment rate with the time dummies.

4.2 Effect of construction activity at the age of 17

The estimation results for the impact of construction activity at age 17 on the probability

of high-school dropout are displayed in Table 5. The differential impact of construction

activity at 17 on males with respect to females is positive and significant. The point

sampled individuals live in their province of birth.

14

estimate is 1.836. The estimated coefficient remains relatively stable as we include more

and more controls in the regression. This can be interpreted as a one standard deviation

increase in construction over total value added inducing an increase of 0.75 percentage

points in the probability of dropping out of high-school for the average male individual.

This effect is consistent with the contemporaneous one in the sign, but it is smaller in

magnitude. The estimated coefficients for the rest of controls are extremely similar to the

ones obtained in the contemporaneous regression.

In unreported regressions, the impact of construction activity at 18 is estimated. Re-

sults show an slightly smaller point estimate with respect to the effect of construction

activity at 17.

4.3 Construction activity measured by the number of new dwellings

In order to test the consistency of the results under different measures of construction

activity, the analysis is repeated substituting construction over total value added by num-

ber of new dwellings over population aged 18-24 in the province. The estimation results

for the contemporaneous effect are displayed in Table 6. The point estimate for the

differential effect of construction activity as measured by number of new dwellings over

population aged 18 to 24 on males versus females is 3.222. This is equivalent to a marginal

effect of 0.6 when the covariates are evaluated at their mean values. This implies that

one standard deviation increase in the ratio of new dwellings over number of individuals

aged 18-24 increases the probability of high-school dropout by 1.37 percentage points on

average. The rest of coefficients have extremely similar magnitudes and significance levels

to the ones reported for the contemporaneous effect of construction activity as measured

by construction value added.

The results for the estimation of the effect of construction activity at 17 as measured by

number of new dwellings can be found in Table 7. The point estimate for the differential

effect of construction activity at 17 on males versus females is 1.873 which is equivalent

to a marginal effect evaluated at the mean values of the covariates of 0.3. Intuitively, this

15

means that an increase by one standard deviation in the ratio of new dwellings at the age

per one hundred individuals aged 18-24 increases the probability of high-school dropout

by 0.57 percentage points. The rest of coefficients are very similar to the ones displayed

in Table 5.

In general, the estimations of the contemporaneous and at age 17 effects are robust to

the use of different measures of construction activity.

4.4 Effects of changes in other economic sectors

The Spanish housing boom was contemporaneous to an important economic upturn. Pro-

duction increased and employment was created, not only in construction but also in the

rest of economic sectors. Hence, other sectors may have played a role in explaining educa-

tion decisions. This is partly taken into account in the previous estimations by including

total value added, youth unemployment rate and unemployment rate as controls in the

specifications. However, as all other sectors affect differently males and low educated

workers, if some other sector’s share in total value added is correlated with the construc-

tion share, the evolution of that other sector could be behind our results.

The empirical exercise is repeated for each of the other economic sectors including each

sector’ share in total value added instead of the construction share. Given the correlations

between sector shares in total value added displayed in Table 8, other sector’s level of

activity could explain the results found for the construction sector in the following cases:

(i) agriculture activity has a positive impact on high-school dropouts for males versus

females, (ii) industry activity has a negative effect on male with respect to female dropout,

(iii) energy activity increases the likelihood of dropout for males versus females and, (iv)

services activity has a positive and strong impact on male relative to female high-school

dropouts.

Figure 8 represents, for each economic sector, the share of male workers as well as the

proportions of male and female workers that are high-school dropouts. The agriculture,

energy and industry sectors are characterized by the prevalence of male workers while

16

the service sector employs the majority of working women. Regarding male high-school

dropouts, agriculture and industry have relatively high incidence of male high-school

dropouts while energy and services have relatively low male high-school dropout rates.

Female dropouts rates are high in agriculture, medium in services and low in energy and

construction.

Given the employment composition described above, other sectors’ effect in the prob-

ability of high-school dropout are supportive of our main hypothesis and, therefore, con-

sistent with the results found for the construction sector in the following cases: (i) the

agriculture and industry sector have a positive impact on high-school dropouts for males

versus females (an increase in agriculture or industry activity constitutes and improve-

ment in labor conditions for low educated men), (ii) the energy sector have a negative

impact on high-school dropouts for males versus females (an increment in energy activity

enhances employment opportunities for highly educated men), or (iii) the service sector

has a negative impact on high-school dropouts for males versus females (services growth

is associated with better labor market prospects for highly educated men and better

employment opportunities for all low and highly educated women).

Table 9 displays the results of the estimations using other sectors’ level of activity and

their interaction with male as alternative explanatory variables. I find that the coefficients

for agriculture, industry and energy are statistically insignificant while the service sector

has a negative influence on high-school dropouts. Hence, the alternative explanation in

which other sectors are responsible for the effect found for construction does not apply in

this context. However, the results found for the service sector could support the hypothesis

that improvements in labor market perspectives for low educated workers induce more

dropouts while increases in expected gains of education cause less dropouts13.

The regression simultaneously including estimates for the differential effect of con-

struction and services activities on males versus females gives almost indistinguishable

13One should interpret this last result with caution given that, differently from the construction sector,the variation experienced by services is not argued to be exogenous

17

estimates from the ones found when studying the construction and services effects sepa-

rately. This can be explained by the low correlation between the construction and service

share in value added. It constitutes additional evidence that changes in the service sector

are unlikely to have any impact in our results for construction but have their own effect

instead.

5 Conclusion

The European Commission in its 2010 Report on Education Policies states that ”targeted

measures for preventing [high-school dropout] should be further mainstreamed”. To guar-

antee the efficiency of education policies that aim at reducing the incidence of high-school

dropout, it is important to understand the determinants of the decision to drop out. This

paper shows that labor market shocks that improve potential labor earnings for low ed-

ucated relative to high educated workers increase the probability of dropping out from

high-school.

The effect is identified by means of the increase in employment opportunities for

low educated male workers provoked by the housing boom in Spain. The housing boom

constituted a worldwide phenomenon caused by financial and demand factors that affected

local labor markets. The Spanish housing boom is a great opportunity to identify labor

market changes due to the big magnitude of the boom and its heterogenous incidence

across regions.

The estimated effect is such that a one percentage point increase in the construc-

tion share in total value added increases the probability of high-school dropout for males

by 1.31% while it leaves the probability of high-school dropout for females unaffected.

Additionally, a one percentage point increase in construction activity at the age of 17,

i.e., when the individual was more likely to be in high-school, increases the probability

of high-school dropout for males from 18 to 24 years old by 0.75% while it leaves the

probability of high-school dropout for females unchanged. These findings are consistent

18

with the estimations obtained using number of new dwellings as an alternative measure

of construction activity. I have ruled out the possibility that the estimated results are the

reflection of changes in other economic sectors. Additional results for the impact of ser-

vices activity on male versus female probability of high-school dropout provide additional

support for my hypothesis.

These conclusions suggest that individual schooling decisions largely respond to labor

market conditions even when the changes in those conditions are transitory. Hence, policy

designers should strengthen their efforts to incentive individuals to get more education in

the presence of booms in economic sectors that use low educated workers intensively.

19

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22

Figures

Figure 1: House prices over time

Data source: Spanish Ministry of Housing. Each data point corresponds to average house prices in

the second quarter of the year for the period 1995 to 2009.

23

Figure 2: Ratio of construction value added and GDP over time

Data source: Spanish National Accountings. This information can be found at the National Statistics

Institute webpage at the address http://www.ine.es/jaxiBD/tabla.do?per=03&type=db&divi=CNTR&idtab=1.

Each data point corresponds to the second quarter of each year for the period 1995 to 2009.

24

Figure 3: Number of constructed dwellings over time

Data source: Spanish Ministry of Housing. The data points are the result of adding up non-

subsidized and subsidized dwellings. Information on non-subsidized dwellings is accessible online from

http://www.mviv.es/es/index.php?option=com content&task=view&id=379&Itemid=434 and data on

subsidized dwellings can be found at http://www.mviv.es/es/index.php?option=com content&task=view&id=318

&Itemid=431. This data covers the period 1991 to 2008.

25

Figure 4: Number of workers in the construction sector over time

Data source: Spanish Labor Force Survey. Each data point corresponds to the second quarter of each

year for the period 1980 to 2007.

26

Figure 5: Average wages over time

Data source: Labor Costs Quarterly Survey (Encuesta Trimestral de Coste Laboral). Each data

point corresponds to the second quarter of each year for the period 2000 to 2008.

27

Figure 6: Correlation between dropout rate and constructionactivity averaged by province

0.0

5.1

.15

.2

.06 .08 .1 .12 .14construction over total value added

Dropout rate Fitted values

Males

0.0

5.1

.15

.06 .08 .1 .12 .14construction over total value added

Dropout rate Fitted values

Females

The estimated slope for men is 0.55 and is significant at the 10% level while the estimated slope for

women is 0.25 and is statistically indistinguishable from zero. The data on dropout rates is computed

using the Spanish Labor Force Survey. The data on construction over total value added is obtained from

the Spanish Regional Accounts. Averages by province are computed over the period 1995 to 2007.

28

Figure 7: Number of male and female construction workers overtime

Data source: Spanish Labor Force Survey. Each data point corresponds to the second quarter of each

year for the period 1980 to 2007.

29

Figure 8: Gender and high-school dropout rates across sectors

30

Data source: Spanish Labor Force Survey. The data corresponds to the second quarter of each year

for the period 1997 to 2007. The share of dropouts is computed as the proportion of workers that do not

have a high-school degree and declare not to have received any education or training in the previous four

weeks.

32

Tables

Table 1: Descriptive statistics for the contemporaneous effectestimations

Variable Mean Std. Dev. Min. Max.high-school dropout 0.314 0.464 0 1construction VA by male 0.05 0.053 0 0.202construction VA 0.099 0.027 0.04 0.202new dwellings by male 0.019 0.025 0 0.152new dwellings 0.037 0.023 0.006 0.152male 0.51 0.5 0 1total VA 2.07e+07 2.98e+07 6.21e+05 1.67e+08age 21.083 1.985 18 24immigrant 0.03 0.172 0 1father high-school grad 0.119 0.324 0 1mother high-school grad 0.127 0.333 0 1father university grad 0.094 0.291 0 1mother university grad 0.09 0.286 0 1father working 0.58 0.494 0 1mother working 0.332 0.471 0 1absent father 0.231 0.421 0 1absent mother 0.072 0.259 0 1emancipated 0.055 0.228 0 1brothers 0.679 0.792 0 9sisters 0.598 0.743 0 10siblings aged 0 to 10 0.11 0.365 0 7siblings aged 11 to 15 0.208 0.451 0 5siblings aged 16 to 20 0.355 0.562 0 5siblings aged 21 to 30 0.566 0.739 0 6dropout siblings 0.275 0.61 0 7youth unemployment rate 0.134 0.055 0.006 0.361unemployment rate 0.06 0.026 0.015 0.152province 26.334 14.334 1 52year 2001.416 3.123 1997 2007

The number of included observations is 186466. The individual data is obtained from the Spanish

Labor Force Survey. The information on value added is collected from the Spanish Regional Accounts and

the data on number of new dwellings can be found in the webpage of the Spanish Ministry of Housing.

33

Table 2: Descriptive statistics for the effect of construction ac-tivity at age 17 estimations. Construction activity measured byvalue added

Variable Mean Std. Dev. Min. Max.high-school dropout 0.294 0.456 0 1construction VA at 17 by male 0.047 0.049 0 0.189construction VA at 17 0.093 0.023 0.04 0.189male 0.509 0.5 0 1construction VA 0.116 0.024 0.061 0.202total VA at 17 1.85e+07 2.65e+07 5.71e+05 1.56e+08age 21.103 1.994 18 24immigrant 0.045 0.208 0 1father high-school grad 0.138 0.344 0 1mother high-school grad 0.179 0.383 0 1father university grad 0.092 0.289 0 1mother university grad 0.111 0.314 0 1father working 0.523 0.499 0 1mother working 0.366 0.482 0 1absent father 0.31 0.463 0 1absent mother 0.087 0.281 0 1emancipated 0.069 0.253 0 1brothers 0.598 0.737 0 9sisters 0.523 0.685 0 7siblings aged 0 to 10 0.107 0.365 0 6siblings aged 11 to 15 0.189 0.432 0 5siblings aged 16 to 20 0.298 0.513 0 4siblings aged 21 to 30 0.49 0.681 0 5dropout siblings 0.21 0.525 0 7youth unemployment rate 0.109 0.038 0.006 0.261unemployment rate 0.048 0.018 0.015 0.119province 26.277 14.308 1 52year 2004.312 1.719 2002 2007


Labor Force Survey. The information on value added is collected from the Spanish Regional Accounts.

34

Table 3: Descriptive statistics for the effect of construction ac-tivity at age 17 estimations. Construction activity measured bynumber of new dwellings

Table 1: Summary statistics

Variable Mean Std. Dev. Min. Max.high-school dropout 0.297 0.457 0 1new dwellings at 17 by male 0.014 0.019 0 0.152new dwellings at 17 0.028 0.019 0.002 0.152male 0.509 0.5 0 1new dwellings 0.042 0.024 0.007 0.152total VA at 17 1.53e+07 2.4e+07 0 1.56e+08age 21.103 1.987 18 24immigrant 0.035 0.183 0 1father high-school grad 0.129 0.335 0 1mother high-school grad 0.148 0.355 0 1father university grad 0.094 0.292 0 1mother university grad 0.097 0.296 0 1father working 0.561 0.496 0 1mother working 0.339 0.473 0 1absent father 0.254 0.435 0 1absent mother 0.078 0.268 0 1emancipated 0.06 0.237 0 1brothers 0.644 0.765 0 9sisters 0.567 0.72 0 10siblings aged 0 to 10 0.107 0.362 0 6siblings aged 11 to 15 0.199 0.44 0 5siblings aged 16 to 20 0.329 0.54 0 5siblings aged 21 to 30 0.538 0.717 0 6dropout siblings 0.241 0.565 0 7youth unemployment rate 0.121 0.047 0.006 0.361unemployment rate 0.054 0.022 0.015 0.13province 26.332 14.324 1 52year 2002.593 2.57 1999 2007


Labor Force Survey. The information on number of new dwellings can be found in the webpage of the

Spanish Ministry of Housing. The missing values in the variable total value added at 17 are dummied

out.

35

Table 4: Contemporaneous effect. Construction activity mea-sured by value added

basic individual family unemployment province(1) (2) (3) (4) (5)

construction VA by male 1.946 2.060 2.871 2.829 2.657(0.499)∗∗∗ (0.503)∗∗∗ (0.68)∗∗∗ (0.671)∗∗∗ (0.686)∗∗∗

construction VA 4.579 4.555 2.668 2.464 2.143(2.434)∗ (2.453)∗ (1.757) (1.596) (1.206)∗

male 0.465 0.461 0.603 0.61 0.634(0.057)∗∗∗ (0.057)∗∗∗ (0.073)∗∗∗ (0.071)∗∗∗ (0.074)∗∗∗

The dependent variable is equal to one if the individual is a high-school dropout and 0 otherwise.

Construction value added is measured relative to total value added in the province. The coefficients

are marked with * if the level of significance is between 5% and 10%, ** if the level of significance is

between 1% and 5% and *** if the level of significance is less than 1%. All the regressions contain

year dummies. The basic regression includes construction value added interacted by male, construction

value added, a male dummy and total value added. The second column adds individual characteristics

to the basic specification, including age binary indicators and an immigrant dummy. The third column

includes, in addition to the controls in column 2, family characteristics, namely, father and mother high-

school graduate dummies, father and mother university graduate indicators, father and mother working

dummies, binary variables equal to one if the parent is present in the household, an emancipated dummy,

number of brothers, number of sisters, number of siblings aged less than 10, number of siblings aged 11

to 15, number of siblings aged 16 to 20, number of siblings aged 21 to 30 and number of high-school

dropout siblings. The fourth column adds youth and total unemployment rates. Finally, the fifth column

includes all previously mentioned controls plus province binary indicators. The individual data is obtained

from the Spanish Labor Force Survey. The information on value added is part of the Spanish National

Accounting data and can be found in the webpage of the Instituto Nacional de Estadıstica. The sample

contains individuals aged 18 to 24 surveyed in the fourth quarter of each year. The time period comprises

the years from 1997 to 2007. Standard errors are clustered at the province level. The number of included

observations is 186466.

36

Table 5: Effect of construction activity at age 17. Constructionactivity measured by value added


construction VA at 17 by male 2.402 2.440 2.074 2.020 1.836(0.857)∗∗∗ (0.885)∗∗∗ (1.076)∗ (1.059)∗ (1.042)∗

construction VA at 17 -8.125 -4.372 -3.934 -3.604 0.097(1.486)∗∗∗ (2.265)∗ (1.769)∗∗ (1.796)∗∗ (1.637)

male 0.462 0.472 0.725 0.733 0.758(0.09)∗∗∗ (0.093)∗∗∗ (0.113)∗∗∗ (0.11)∗∗∗ (0.109)∗∗∗


Construction value added is measured relative to total value added in the province. The coefficients


between 1% and 5% and *** if the level of significance is less than 1%. All regressions contain year

dummies. The basic regression includes construction value added at 17 interacted by male, construction

value added at 17, a male dummy, total value added at 17 and construction value added. The second

column adds individual characteristics to the basic specification, including age binary indicators and

an immigrant dummy. The third column includes, in addition to the controls in column 2, family

characteristics, namely, father and mother high-school graduate dummies, father and mother university

graduate indicators, father and mother working dummies, binary variables equal to one if the parent

is present in the household, an emancipated dummy, number of brothers, number of sisters, number of

siblings aged less than 10, number of siblings aged 11 to 15, number of siblings aged 16 to 20, number

of siblings aged 21 to 30 and number of high-school dropout siblings. The fourth column adds youth

and total unemployment rates. Finally, the fifth column includes all previously mentioned controls plus

province binary indicators. The individual data is obtained from the Spanish Labor Force Survey. The

information on value added is part of the Spanish National Accounting data and can be found in the

webpage of the Instituto Nacional de Estadıstica. The sample contains individuals aged 18 to 24 surveyed

in the fourth quarter of each year. The time period comprises the years from 2002 to 2007. Standard

errors are clustered at the province level. The number of included observations is 86953.

37

Table 6: Contemporaneous effect. Construction activity mea-sured by number of new dwellings


new dwellings by male 1.838 1.980 3.222 3.310 3.222(0.583)∗∗∗ (0.599)∗∗∗ (0.988)∗∗∗ (0.942)∗∗∗ (0.988)∗∗∗

new dwellings 2.255 1.943 -.570 2.557 -.570(1.839) (1.898) (0.592) (1.173)∗∗ (0.592)

male 0.587 0.589 0.776 0.766 0.776(0.031)∗∗∗ (0.032)∗∗∗ (0.043)∗∗∗ (0.041)∗∗∗ (0.043)∗∗∗


Number of new dwellings is measured relative to population aged 18 to 24 in the province. The coefficients



dummies. The basic regression includes number of new dwellings interacted by male, number of new

dwellings, a male dummy and total value added. The second column adds individual characteristics to

the basic specification, including age binary indicators and an immigrant dummy. The third column

includes, in addition to the controls in column 2, family characteristics, namely, father and mother high-

school graduate dummies, father and mother university graduate indicators, father and mother working

dummies, binary variables equal to one if the parent is present in the household, an emancipated dummy,

number of brothers, number of sisters, number of siblings aged less than 10, number of siblings aged 11 to

15, number of siblings aged 16 to 20, number of siblings aged 21 to 30 and number of high-school dropout

siblings. The fourth column adds youth and total unemployment rates. Finally, the fifth column includes

all previously mentioned controls plus province binary indicators. The individual data is obtained from

the Spanish Labor Force Survey. The information on number of new dwellings can be found in the

webpage of the Spanish Ministry of Housing. The sample contains individuals aged 18 to 24 surveyed in

the fourth quarter of each year. The time period comprises the years from 1997 to 2007. Standard errors

are clustered at the province level. The number of included observations is 186466.

38

Table 7: Effect of construction activity at age 17. Constructionactivity measured by number of new dwellings


new dwellings at 17 by male 1.629 1.778 1.588 2.050 1.873(0.788)∗∗ (0.753)∗∗ (0.864)∗ (0.966)∗∗ (1.009)∗

new dwellings at 17 -1.866 1.417 1.334 1.765 0.608(2.302) (2.077) (1.946) (1.421) (1.457)

male 0.636 0.641 0.745 0.861 0.873(0.033)∗∗∗ (0.033)∗∗∗ (0.036)∗∗∗ (0.039)∗∗∗ (0.041)∗∗∗


Number of new dwellings is measured relative to population aged 18 to 24 in the province. The coefficients



dummies. The basic regression includes number of new dwellings at 17 interacted by male, number

of new dwellings at 17, a male dummy, total value added at 17 and number of new dwellings. The

second column adds individual characteristics to the basic specification, including age binary indicators

and an immigrant dummy. The third column includes, in addition to the controls in column 2, family

characteristics, namely, father and mother high-school graduate dummies, father and mother university

graduate indicators, father and mother working dummies, binary variables equal to one if the parent

is present in the household, an emancipated dummy, number of brothers, number of sisters, number of

siblings aged less than 10, number of siblings aged 11 to 15, number of siblings aged 16 to 20, number

of siblings aged 21 to 30 and number of high-school dropout siblings. The fourth column adds youth

and total unemployment rates. Finally, the fifth column includes all previously mentioned controls plus

province binary indicators. The individual data is obtained from the Spanish Labor Force Survey. The

information on number of new dwellings can be found in the webpage of the Spanish Ministry of Housing.

The sample contains individuals aged 18 to 24 surveyed in the fourth quarter of each year. The time

period comprises the years from 1999 to 2007. Standard errors are clustered at the province level. The

number of included observations is 143440.

39

Table 8: Correlations between value added shares of economicsectors

Agriculture Industry Energy Construction ServicesAgriculture 1Industry -0.1614 1Energy 0.0182 -0.1571 1Construction 0.0934 -0.4416 0.0816 1Services -0.4906 -0.6543 -0.2342 0.0059 1

Data source: Spanish Regional Accounts. The correlation is computed for the sample included in the

regression for the contemporaneous effect.

40

Table 9: Contemporaneous effect. Other sectors activity mea-sured by value added

agriculture industry energy services(1) (2) (3) (4)

VA by male -.746 0.167 0.645 -.746(0.305)∗∗ (0.303) (0.718) (0.305)∗∗

VA -.651 1.440 -2.034 -.651(0.708) (0.876) (1.215)∗ (0.708)

male 1.383 0.871 0.876 1.383(0.198)∗∗∗ (0.054)∗∗∗ (0.034)∗∗∗ (0.198)∗∗∗

The dependent variable is equal to one if the individual is a high-school dropout and 0 otherwise. Each

sector value added is measured relative to total value added in the province. The coefficients are marked

with * if the level of significance is between 5% and 10%, ** if the level of significance is between 1% and

5% and *** if the level of significance is less than 1%. All equations include the same controls, namely,

total value added, age binary indicators, an immigrant dummy, family characteristics including father

and mother high-school graduate dummies, father and mother university graduate indicators, father

and mother working dummies, binary variables equal to one if the parent is present in the household, an

emancipated dummy, number of brothers, number of sisters, number of siblings aged less than 10, number

of siblings aged 11 to 15, number of siblings aged 16 to 20, number of siblings aged 21 to 30, number

of high-school dropout siblings, youth and total unemployment rates, year dummies and province binary

indicators. The individual data is obtained from the Spanish Labor Force Survey. The information on

value added is part of the Spanish National Accounting data and can be found in the webpage of the

Instituto Nacional de Estadıstica. The sample contains individuals aged 18 to 24 surveyed in the fourth

quarter of each year. The time period comprises the years from 1997 to 2007. Standard errors are

clustered at the province level. The number of included observations is 186466.

41

Appendix A: Contemporaneous effect displaying con-

trols. Construction activity measured by value added


19 years old 0.236 0.296 0.297 0.298(0.016)∗∗∗ (0.018)∗∗∗ (0.018)∗∗∗ (0.018)∗∗∗

20 years old 0.33 0.395 0.395 0.403(0.021)∗∗∗ (0.025)∗∗∗ (0.025)∗∗∗ (0.025)∗∗∗

21 years old 0.392 0.409 0.411 0.421(0.022)∗∗∗ (0.024)∗∗∗ (0.024)∗∗∗ (0.024)∗∗∗

22 years old 0.398 0.374 0.377 0.387(0.023)∗∗∗ (0.026)∗∗∗ (0.026)∗∗∗ (0.027)∗∗∗

23 years old 0.418 0.334 0.336 0.348(0.025)∗∗∗ (0.028)∗∗∗ (0.029)∗∗∗ (0.029)∗∗∗

24 years old 0.454 0.314 0.317 0.329(0.029)∗∗∗ (0.034)∗∗∗ (0.035)∗∗∗ (0.036)∗∗∗

immigrant 0.579 0.041 0.054 0.062(0.093)∗∗∗ (0.062) (0.061) (0.061)

father high-school grad -.949 -.949 -.924(0.032)∗∗∗ (0.032)∗∗∗ (0.031)∗∗∗

mother high-school grad -.940 -.936 -.906(0.03)∗∗∗ (0.029)∗∗∗ (0.028)∗∗∗

father university grad -1.708 -1.713 -1.706(0.05)∗∗∗ (0.05)∗∗∗ (0.051)∗∗∗

mother university grad -1.885 -1.882 -1.863(0.058)∗∗∗ (0.058)∗∗∗ (0.057)∗∗∗

father working -.299 -.282 -.287(0.019)∗∗∗ (0.018)∗∗∗ (0.018)∗∗∗

mother working -.043 -.027 -.028(0.018)∗∗ (0.018) (0.017)

absent father -.098 -.086 -.071(0.024)∗∗∗ (0.024)∗∗∗ (0.02)∗∗∗

absent mother 0.327 0.337 0.354(0.044)∗∗∗ (0.045)∗∗∗ (0.047)∗∗∗

emancipated 0.579 0.575 0.519(0.054)∗∗∗ (0.054)∗∗∗ (0.055)∗∗∗

brothers -.757 -.757 -.741(0.033)∗∗∗ (0.033)∗∗∗ (0.031)∗∗∗

sisters -.656 -.659 -.643(0.032)∗∗∗ (0.032)∗∗∗ (0.031)∗∗∗

siblings 0 to 10 1.317 1.308 1.260(0.038)∗∗∗ (0.038)∗∗∗ (0.035)∗∗∗

siblings 11 to 15 0.912 0.902 0.862(0.034)∗∗∗ (0.034)∗∗∗ (0.03)∗∗∗

siblings 16 to 20 0.522 0.515 0.492(0.031)∗∗∗ (0.03)∗∗∗ (0.03)∗∗∗

siblings 21 to 30 0.258 0.255 0.248(0.029)∗∗∗ (0.029)∗∗∗ (0.027)∗∗∗

dropout siblings 1.319 1.316 1.292(0.025)∗∗∗ (0.026)∗∗∗ (0.025)∗∗∗

total value added -1.19e-09 -1.37e-09 4.71e-10 5.84e-10 2.78e-09(1.63e-09) (1.65e-09) (1.13e-09) (1.03e-09) (1.53e-09)∗

youth unemployment rate 1.006 0.144(0.639) (0.298)

unemployment rate 1.094 -1.606(1.648) (0.964)∗


The coefficients are marked with * if the level of significance is between 5% and 10%, ** if the level of

significance is between 1% and 5% and *** if the level of significance is less than 1%. All the regressions

contain year dummies. The individual data is obtained from the Spanish Labor Force Survey. The sample

contains individuals aged 18 to 24 surveyed in the fourth quarter of each year. The time period comprises

the years from 1997 to 2007. Standard errors are clustered at the province level. The number of included

observations is 186466.

42

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